The industry will be experiencing significant operational challenges in the coming years. These challenges cover a wide gamut of topics. Flashover LLC was contracted by Electric Power Research Institute (ERPI) to conduct an informal industry survey to identify these operational challenges. The survey attempted to collect industry concerns in an operating environment timeframe to the engineering planning, outage coordination, and forecasting responsibilities to address identified issues and concerns. These responses will help guide EPRIs future efforts to develop a coherent strategic vision in its mission to support system operations. The presentation for this session will focus on the results of that survey. Room: 401, Bldg: ITE, 371 Fairfield Way, Storrs, Connecticut, United States

In this talk, I will share our recent progress on developing learning algorithms for real-world energy system control, with stability and computational tractability guarantees. The first part is on reinforcement learning for power grid control. I will introduce a novel neural network architecture – monotone neural network (MNN) that ensure the network output is a monotone function of the input. MNN is achieved by first designing neural networks that are convex (with universal approximation guarantee) and using gradients of convex functions to ensure monotonicity. We show that MNN is a powerful structure for voltage control – with stability and optimality guarantees compared to standard neural networks. The second part is about operator learning for building control. There is an emergent need to model indoor air quality to improve occupant health and building energy efficiency. A fundamental challenge is that building airflow dynamics are governed by nonlinear partial differential equations (PDEs) with unknown parameters, which are computationally prohibitive from a real‑time control perspective. I will introduce our work on PDE‑constrained optimization for building model identification and designing neural operator learning for efficient PDE system control. Speaker(s): , Yuanyuan Shi 371 Fairfield Way, Ite 336, Storrs, Connecticut, United States, 06269-0001, Virtual: https://events.vtools.ieee.org/m/478817